Real Time Mine Monitoring System and Method

ABSTRACT

The present invention relates to a method for detecting changes in the ore grade of a rock face in near real time. The method includes the step of providing a scanning system having at least a hyperspectral imager, a position system, a LiDAR or range determination unit and computational resources. Further, the method involves determining a precise location of the scanning system utilising the position system. The rock face is scanned with the range determination unit to determine rock face position information. The method involves scanning the rock face with the hyperspectral imager to produce a corresponding rock face hyperspectral image. Further the method involves utilising the computational resources to fuse together the rock face position information and the corresponding rock face hyperspectral image to produce a rock face position and content information map of the rock face.

FIELD OF THE INVENTION

The present invention provides for systems and methods for the real timemonitoring of mining operations.

REFERENCES

-   1. Durrant-Whyte H, Geraghty R, Pujol F, Sellschop R. How digital    innovation can improve mining productivity. McKinsey Co Insights    [Internet]. 2015; (Nov.):1-13. Available from:    http://www.mckinsey.com/insights/energy_resources_materials/how_digital_innovation_can_improve_minin    g_productivity-   2. Job A T, Edgar M L, McAree P R. Real-time shovel-mounted coal or    ore sensing. Iron Ore 2017. 2017;(July):397-406.-   3. Schneider S, Melkumyan A, Murphy R J, Nettleton E. A geological    perception system for autonomous mining. Proc - IEEE Int Conf Robot    Autom. 2012;2986-91.-   4. Gallie E A, McArdle S, Rivard B, Francis H. Estimating sulphide    ore grade in broken rock using visible/infrared hyperspectral    reflectance spectra. Int J Remote Sens [Internet]. 2002; 23:11(May    2012):2229-46. Available from:    http://dx.doi.org/10.1080/01431160110075604-   5. Murphy R J, Monteiro S T, Schneider S. Evaluating classification    techniques for mapping vertical geology using field-based    hyperspectral sensors. IEEE Trans Geosci Remote Sens. 2012;    50(8):3066-80.-   6. Murphy R J. Evaluating simple proxy measures for estimating depth    of the ˜1900 nm water absorption feature from hyperspectral data    acquired under natural illumination. Remote Sens Environ. 2015;    166:22-33.-   7. Murphy R J, Schneider S, Monteiro S T. Mapping layers of clay in    a vertical geological surface using hyperspectral imagery:    Variability in parameters of SWIR absorption features under    different conditions of illumination. Remote Sens. 2014;    6(9):9104-29.-   8. Murphy R J, Schneider S, Taylor Z, Nieto J. Mapping clay minerals    in an open-pit mine using hyperspectral imagery and automated    feature extraction. Eur J Remote Sens. 2015; 48(1):479-87.

BACKGROUND OF THE INVENTION

Any discussion of the background art throughout the specification shouldin no way be considered as an admission that such art is widely known orforms part of common general knowledge in the field.

Mining companies have identified that real-time accurate orebodyknowledge is of significant economic value. In particular, hyperspectralimaging has been highlighted as a key technique to enable real-timeorebody knowledge for precision ore mining. However, while there aremany existing commercially available off-the-shelf (COTS) hyperspectralsensors, none of these sensors are Fit-For-Purpose (FFP) for routinemine face mapping. To make these COTS sensors FFP requires significantsystem customisation to convert raw data streams to useablegeo-spatially accurate geological face maps.

SUMMARY OF THE INVENTION

It is an object of the invention, in its preferred form to provide anintegrated geological mapping platform, that can be used on a dailybasis, in real-time on mine sites covering the full range of minedcommodities.

In accordance with a first aspect of the present invention there isprovided a method for detecting changes in the ore grade of a rock facein near real time, the method including the steps of: providing ascanning system having at least a hyperspectral imager, a positionsystem, a LiDAR or range determination unit and computational resources;determining a precise location of the scanning system utilising theposition system; scanning the rock face with the range determinationunit to determine rock face position information; scanning the rock facewith the hyperspectral imager to produce a corresponding rock facehyperspectral image; and utilising the computational resources to fusetogether the rock face position information and the corresponding rockface hyperspectral image to produce a rock face position and contentinformation map of the rock face.

Preferably, the scanning step includes forming a point cloud of the rockface position and the method includes determining a content informationmap for points of the point cloud. Preferably, the point cloud positionis refereced relative to the precise location of the positioning system.

In some embodiments, the hyperspectral image sensors are calibrated tomitigate the impact of at least one of dark current, smile, keystone,bad pixels and other sensor specific errors.

In some embodiments, the method also includes simultaneously sensing theatmospheric lighting conditions and processing the capturedhyperspectral image to account for lighting conditions.

In some embodiments, the method also includes utilising machine learningalgorithms to classify the material in the content information map.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of exampleonly, with reference to the accompanying drawings in which:

FIG. 1 illustrates an initial prototype system;

FIG. 2 is an example photo of the prototype system as mounted on a traytruck;

FIG. 3 illustrates an example Surveyor web interface showing commandmodule where the user specifies survey angle bounds and cameraintegration times.

FIG. 4 and FIG. 5 illustrate the mean signal in the atmosphericabsorption bands as a function of the x position of the surveyed scene.Each survey was performed with different integration times and differentangle bounds, leading to different gradients and lengths of each line.

FIG. 6 illustrates typical relative reflectance spectra of coal,swelling clay (smectite) and non-swelling clay (kaolinite).

DETAILED DESCRIPTION

The preferred embodiments provide for the ability to map ore faces, inreal-time which is of significant economic value to the global miningindustry. The preferred embodiments provide for a systems ability toprovide requisite information for real-time decision support systems toenhance mine yield and pit-to-port performance, as well as providing theguidance system necessary for fully autonomous mining operations.

The preferred embodiments provide a face mapping system, combiningLiDAR, RTK GPS and hyperspectral imaging cameras.

The results provide for a real-time, fusion of spatial and spectralinformation. Fused datasets for new information not otherwise capablefrom a standalone system. The purpose of the system is to be able toscan mining terrain in real-time and determine from the results ofprocessing of sensor inputs, the material properties of the terrain.

By providing this information, several benefits could be realised by themine-site in real-time. For example, the information could be relayed tomining machinery, such as excavators, for real-time precision machineguidance, and the information could be used in real-time decisionsupport systems to optimise the flow of material into the minesprocessing facility, or to discriminate accurately between ore and wastematerial, or used to make determinations of stockpile characteristicsfor processing plant blend optimisation, or for the maximisation ofresource recovery.

The major components of the preferred embodiments include: 1. LiDAR orstereo-imaging; 2. High precision GPS, 3. Hyperspectral imaging sensors,4. High definition RGB, 5. scanning head, 6. On-board computing and 7.Wi-fi data transfer.

A brief summary of how the system works is as follows:

1. Determining the precise location of the scanning system using GPS,

2. Scanning the terrain with a LiDAR or stereo-imaging equipment,

3. Creating a detailed spatial map of the terrain with each point in thepoint cloud referenced to the GPS coordinate system,

4. Scanning the terrain with hyperspectral imaging sensors, that havebeen calibrated to mitigate the impact of dark current and smile andkeystone and bad pixels and other sensor specific errors,

5. Using an algorithm to map the precise location of obtainedhyperspectral pixel information to a GPS referenced position in thepreviously determined point cloud

6. Using a Fibre Optic Downwelling Irradiance Sensor, or empiricallydetermined algorithm, or empirically captured scene reference data toadjust the captured hyperspectral information for impacts of theatmosphere, if using sunlight as the light source

7. If using an artificial light source, then removing the spectral inputprofile of the artificial light source from the captured data,

8. If using a combination of natural and artificial light then using analgorithm that accounts for the proportional impacts of each lightsource on the captured hyperspectral data

9. Using the resulting captured information, applying an algorithm orcombination of algorithms that, fuse the textural and structuralinformation captured by the LiDAR or stereo-imaging equipment with thespectral information to make a determination of likely materialcharacteristics,

10. Using the results from the previous step to create a set of spectraland spatial features that may be characteristic of underlying materialproperties,

11. Using the results from the previous step to make a comparison ofspectral and spatial features from a known database of features thatrelate to underlying material properties,

12. Using the result from the previous step to apply an artificialneural network, that makes a prediction of the material properties for apixel or set of pixels

13. Using the result from the previous step to compare the materialproperties against predicted material properties for a block of material

14. Iteratively applying the entire process to make progressively moreaccurate determinations of material properties across an entire miningoperation, including results from processing of material, stockpiles,drill hole data

15. Using the model generated, in real-time, to inform mining equipmenton precise digging location and mine operators the recommended optimaldecision for mining, processing, stockpiling or depositing as wastematerial the identified material.

In summary, the LiDAR scans and captures the scene. Data is fused withRTK-GPS so that each pixel in the scene has a precise geo-referencedcoordinate. The Scene is then captured with RGB images and hyperspectralimages. An Algorithm is applied to accurately add RGB and hyperspectralinformation to each geo-referenced pixel. A further set of algorithmsare then run to:

1. identify structural and textural features form RGB and LiDAR data

2. idenfify spectral features from hyperspectral data

3. combine the structural, textural and hyperspectral data to make aprediction on the material properties

4. use the output of the combined result as an input for the furtheridentification of the structural model, textural model and hyperspectralmodel

5. iterate the process until variation of the learned materialproperties has been minimised

6. output the results to a decision support system.

Example of an Initial Embodiment

An initial simplified embodiment was created which included: Spatialregistration of sensors relative to a survey reference frame; Mapping ofhyperspectral images onto terrain model built from LiDAR measurements;Ray-casting the LIDAR measurements onto a spherical cell grid. Methodfor setting integration time so that utilization of digital number rangeis maximized. Automated reflectance target detection Methods forcorrecting for atmospheric effects. Web-based interface for remotesurvey control (includes wi-fi connection to survey system). Use ofRTK-GPS to provide geo-reference surveys (terrain and hyperspectralimagery). Fusion of hyperspectral images to provide ENVI-compatiblebinary for classification. Multiple classification options givenENVI-formatted data cube. Spectral alignment (i.e. reflectancecontinuity across shared bands of paired cameras.). Hyperspectral datafiltering.

Extensions can include the addition of environment sensor (e.g. tocorrect for atmospheric effects), the addition of video data stream (formore data accuracy). The Transition to improved computational resources.Options (linux base computer for surveyor and logging with windowsvirtual machine, or develop hyspex resource manager for linux eitherthrough HySpex provision of interface library. Fusion with translationstage, with/without LiDAR and HD-RGB data stream.

The initial embodiment provides a system capable of acquiring,processing, and classifying hyperspectral data in the field and in realtime, mapped to terrain and geo-referenced for integration with minemaps. It is an integrated system that performs push-broom scanning tobuild a 3D datacube from two individual line-scanning hyperspectralsensors, and also spatially and spectrally fuses the information toprovide a single datacube for an entire scene.

The initial embodiment is also Field capable (robust), allows forrotation in more than one axis for performing rock face scans, Terrainmapping, Geo-location, Hyperspectral imaging capabilities from visibleto short wave infrared spectrum (400-2500 nm) and is Light vehiclemounted.

Turning initially to FIG. 1 , there is illustrated an overall system 1which is designed to be tray truck mountable. The system 1 includesLiDAR and hyperspectral cameras 2 which are pivot mountable via pivot 3.A high accuracy RTK-GPS system including antennas 4, 5, 6 is alsoprovided. Batteries 8, 9 provide power and three box units 10-12 providefor storage of electronic and computational resources. The system ismounted on base rails 14, which

The camera unit 2 consists of a rotating unit on a stationaryvehicle-mounted platform. The sensor head 2 on the rotating platform 3contains a LiDAR for acquiring terrain data to generate a terrain map,and hyperspectral cameras for completing a survey. RTK-GPS is used togeolocate the acquired data. On-board computers within units 10-12manage data flows and process acquired data, and on-board power 8,9,completes the self-contained system.

FIG. 2 illustrates a photo of a prototype system mounted on a traytruck. The diagrams illustrate the box units 10-12 used to containcomputers, and networking equipment. In addition to the battery units8,9 used to power the system (and one spare); and the enclosure and sunshield for the head unit, which provides some insulation to reduce thetemperature in the head unit while operating in sunlight.

The system can include the following functional components:

Imaging system: The imaging system includes the sensor hardware 1 on therotating head unit 3 which is used to acquire hyperspectral and terraindata. The hyperspectral cameras can be HySpex VNIR1800 (400-1000 nm)camera and a HySpex SWIR384 (1000-2500 nm) camera. Each camera is ahyperspectral line scanner—each image obtained from the camera has onespatial dimension and provides a full spectral signature for each pixelin that spatial dimension. The cameras are operated in a pushbroommanner (rotating or translating the cameras to obtain successive linescans) to build up a datacube with two spatial dimensions and onespectral dimension. As both cameras have separate fore-optics, differentspatial resolutions, and are offset from the centre of rotation of therotating platform, image registration is required and is a non-trivialtask. This is further discussed below.

A secondary challenge associated with the cameras is maximising thedynamic range of the sensor arrays to obtain the best signal to noiseratio during the survey. The sensors are not uniformly sensitive to eachwavelength of light, and solar illumination is also not of uniformintensity across the electromagnetic spectrum. Furthermore, the camerasdo not automatically adjust the integration time (the exposure period).Adjusting integration time to maximise dynamic range but avoidsaturating the sensors is therefore part of the data acquisitionworkflow. Due to the non-uniform nature of solar illumination and thesensitivity of the cameras at different wavelengths, maximising thedynamic range of the cameras across the whole measured spectrum will notprovide a measured radiance curve with uniform signal to noise ratio.The spectra at the ends of the sensor's sensitivity (e.g. greater thanabut 2000 nm for the SWIR camera, and greater than about 750 nm for theVNIR camera) will use less of the dynamic range. Hence, an approach toacquisition can be to take multiple scans where the dynamic rangeutilisation is maximised over different spectral ranges of the cameras,in order to minimise noise in those ranges.

Hyperspectral cameras acquire a measure of the radiance of a pixel thatthey are scanning. The light incident on the sensor is a function ofboth the illumination (light source) and how the incident light reflectson the material (which in turn is both dependant on the material itselfand its orientation relative to the sensor). In order to perform acorrection to compute the reflectance of the material, reflectivetargets of known spectral reflectance are placed in the surveyed scene.These are used to obtain a measure of the illumination and thus correctfor the reflectance of materials in the scene.

A Velodyne VLP-16 LiDAR is used to obtain point-cloud range data andbuild up a terrain map. The VLP-16 is a 16-beam rotating LiDAR,providing 360° range in the rotating axis and 30° view in the azimuthaxis. The LiDAR is mounted sideways so the beams rotate in the verticalplane. Combined with the rotating platform, this gives the bestresolution of the face.

Surveying system: Each hyperspectral camera is a line scanning sensor,indicating it only provides an image in one spatial dimension. Likewise,the LiDAR in its vertical orientation has a limited horizontal field ofview. The surveying system function is to enable the imaging system toscan a complete scene.

The pan-tilt unit (PT-2050) 3 is the rotating base that enables acomplete survey of a scene to be performed with a line-scanning sensor.The ability to tilt also allows the system to build up a composite scenefrom multiple scans at different tilt angles. The unit has an angularresolution of 0.010, which is sufficient precision to capture acontiguous image with the hyperspectral cameras. While capturing thehyperspectral data, the pan speed is synchronised with the frame rate ofthe cameras in order to produce a continuous image.

Geo-referencing system: Three GNSS receivers 4, 5, 6 with RTKcorrections are used to obtain a pose solution to orient the system whentaking field surveys, in order to geolocate the terrain model and data.When both the position of the receivers relative to a reference pointwhich is used as the origin of the system and the position in GPScoordinates is known, an optimisation problem is solved to find thecoordinate of the origin in GPS coordinates and the orientation of thesystem that best fits the receiver locations to the measuredcoordinates. As the terrain model is stored in a coordinate systemrelative to the position of the system, this pose and orientationsolution can also be used to assign coordinates to every point in theterrain map, and thus reference the terrain and hyperspectral data tothe mine map.

Computers and network/information flow: Two embedded computers are usedto operate the system, as the API for controlling the HySpex cameras wasonly provided as a pre-compiled binary for Windows. The remainder of thehardware interfacing and applications are run on a separate Linuxmachine. Data is distributed between the computers via a wired network.An external laptop is used to interface with the system and provide userinput via a web interface.

Each sensor has an associated resource manager that provides aninterface to read data packets via the sensor's communication interface,publish it using the middleware software, and command the sensor (whereapplicable).

The OstkSurveyor is the application which handles the data acquired bythe OreSense system and provides an interface for its functions.

Survey data representation and fusion: The primary data being acquiredduring a survey is the hyperspectral and range data, in order to buildup a terrain map. The other data sources provide supporting metadata,such as GPS positioning for geo-location. The problems associated withdata representation is how to model the terrain, how to fuse the camerasources, and how to map the hyperspectral image to the terrain model.

The system stores data in a two-dimensional grid-based representation,using spherical coordinates. The centre of the spherical coordinates isat the rotational centre of the system, at the base plate 14 on whichthe sensors are mounted. A grid array is generated for each survey,representing azimuth (yaw) angle in the x axis and elevation angle inthe y axis. The pan and tilt angles of the survey and a fixed resolutionare used to generate the extents and size of the grid. To representterrain, each cell of the grid is populated with a radius measurement,obtained from the LiDAR point cloud, so that each cell specifies anazimuth, elevation and radius that locates a point of the terrain inspace.

In order to locate data within the grid, the system software uses theposition and orientation of each sensor relative to the sphericalcoordinate centre to transform and fuse data. Mechanical offsets and therotation of the pan-tilt unit are modelled by the Surveyor. Each sensorthus has an origin in a local coordinate system that moves as thepan-tilt unit rotates.

The LiDAR resource manager converts the LiDAR returns into (x,y,z)coordinates in the frame of reference of the LiDAR. When the Surveyorreceives a packet of LiDAR data, it:

1. Computes the pan and tilt angles of the pan-tilt unit at the time thedata was acquired, in order to compute the position and orientation ofthe LiDAR and its transform to the sphere-centred coordinates.

2. Computes the Cartesian (x,y,z) coordinates of the LiDAR returns inthe sphere-centred coordinates.

3. Converts the sphere-centred cartesian coordinates to sphericalcoordinates (azimuth, elevation, radius).

4. Populates every (azimuth, elevation) cell of the terrain grid withradius information if available from the LiDAR data packet.

Some cells of the grid will be populated more than once with radiusinformation from different returns. In this case, the measurement isadded to a rolling average. Some cells are not populated with any data,in which case the grid is interpolated to fill missing data. This cellgrid then provides the terrain map for the scene that is mapped to thehyperspectral data.

Both hyperspectral cameras are offset from the centre of rotation of thepan-tilt platform. This poses a challenge for registration of the cameraimages with each other. Furthermore, the camera images must also beregistered with the terrain to provide the desired output of a terrainmap with ore grade classifications that can be used to update themineralogical model of the surveyed area. Calibration informationprovided with the cameras gives the field of view of each pixel in thespatial dimension of the sensor. This allows the camera focal point tobe treated as a point source of a series of vectors (one for eachspatial pixel of the sensor), which is then cast to find theintersection with the terrain. Where the ray intersects, the grid ofhyperspectral data is populated with the spectral information from thatspectral pixel of the sensor. As the pan-tilt unit rotates across thescene, this process is repeated for each new frame of data from thecamera. The resulting hyperspectral data is stored in a similar grid ofspherical coordinates to the terrain data, with each cell containing avector of information (the spectral signature measured from that pixel).

This ray casting process generates a grid of hyperspectral data for eachcamera. Each camera's frame rate is synchronised with the rotation ofthe pan-tilt unit in order to create a continuous 2D scene at thespecified spatial resolution. By using grids with the same angle boundsand resolution, this method generates grids which are spatially alignedand have the same resolution, from data generated from cameras withdifferent spatial resolutions and at different locations. This solvesthe spatial fusion problem, but the cameras must also be spectrallyfused. Spectral fusion is mostly addressed by the illuminationcorrection process which calculates the reflectance values of materialsin the scene. However, the cameras have some overlapping wavelengths.This data is simply truncated so that the wavelengths saved in the fusedhyperspectral datacube are contiguous.

Scene Acquisition

Scene acquisition consists of three steps, iterating through some untildesired performance characteristics are met.

1. Initially, the user specifies angle bounds and integration time foreach camera for the scene survey.

2. Perform a scan over the specified angle bounds to obtain lidar dataand generate a terrain map. This is a sweep of the scene with only theLiDAR acquiring data. At this step, the user can check that the anglebounds set for the survey have captured the full scene of interest, thenadjust and reset the survey if not. The terrain grid can be generatedbefore the hyperspectral cameras acquire data, in order to ensure theterrain grid is fully populated before using it to ray cast the spectraldata.

3. Perform a scan with the cameras acquiring data to obtain the 3Dhypercube of the scene mapped to the terrain. Now the cameras acquireline scans as the pan-tilt unit pans across the scene, and the Surveyorapplication fuses the information in the spherical cell-gridrepresentation. The output of this survey is a terrain grid and anENVI-compatible binary and header file with hyperspectral data, which iswritten to the disk of the Surveyor computer on the completion of asurvey.

This process is performed over a small segment of the scene containingthe reflection targets in order to set the integration time so that thedynamic range is best utilised by the cameras in the wavelength regionof interest. The user can observe the saturation of pixels in the sceneusing the visualizer. Pixels which are saturated are highlighted in thescene, and the visualizer can pick pixels and show the spectra for thosepixels, to observe what range of the spectrum is saturated. The sameprocess is used to scan a full excavation face, to obtain the scene tobe classified.

At the completion of a survey, a terrain map (as a point cloud inspherical grid coordinates) and the hyperspectral data (as anENVI-compatible binary) are written to file. The duration of a surveyover a complete face is between 5-10 minutes, depending on the anglerange. Some efficiencies can be gained by improving the computationspeed of the fusion process, which is the limiting factor in the surveyspeed.

The Surveyor web interface is the user interface can include an appwhich manages the survey workflow data. It allows the user to controlthe data acquisition process, as well as on-board post-processing andclassification workflows. This interface allows remote wired or wireless(over WiFi) connection to the system to control the equipment from anexternal computer or tablet. The web interface also provides informationabout the hardware and status of the system.

FIG. 3 illustrates an example surveyor web interface showing commandmodule where the user specifies survey angle bounds and cameraintegration times.

Survey Data Processing

After acquiring and fusing survey data, the hyperspectral data must beprocessed to make it suitable for classification. The system can workwith artificial broadband illumination sources (such as halogen lights),but the field trials and most surveys can be captured under natural(solar) illumination, which leads to additional processing challenges.Processing steps include filtering noisy bands caused by atmosphericwater absorption; computing the reflectance from the at-sensor radiancemeasures; and spectrally aligning the sensors. All these processes canbe automated.

Real-time camera corrections: The data obtained from the hyperspectralcameras has undergone some correction processes by the camera itself,but further corrections may also be required. The camera takes a darkbackground measure at the start of each survey. This is the average ofseveral frames captured while the shutter to the camera is closed and isa measure of the background noise of the sensor. This can beautomatically removed from each frame captured by the camera. The camerasensors undergo a calibration process by the manufacturers, during whichthe non-uniformity of the sensors is measured. This measures variationin the sensitivity of the sensor array to uniform intensity light at thesame spectrum and is also automatically removed from the data that isobtained from the camera. The camera sensors are not uniformly sensitiveto each wavelength of light. This is known as the quantum efficiency ofa given wavelength, and this correction must be applied after the datais received. The quantum efficiency for each wavelength is provided withthe calibration files associated with each camera.

Atmospheric corrections: Water vapour in the atmosphere absorbs light ininfrared regions of the electromagnetic spectrum. In these ranges,incident light is partially or completely absorbed, leading to bands ofnoisy data which must be filtered out. The bands most affected are near970 nm, 1200 nm, 1450 nm, 1950 nm, and 2500 nm. However, the exact rangeof the affected bands depends on weather conditions such as the amountof water vapour or cloud in the atmosphere—on cloudier days or underhumid conditions, more bands are affected. The spectral bands which arefiltered out as part of the atmospheric correction process can beconfiguration parameters.

Dark current artefacts: Before each survey, both cameras take ameasurement of the dark current, which is the response in the sensorswhen frames are captured with the camera shutter closed. This is done bytaking and averaging the signal from 200 frames captured with theshutter closed. This is intended to ensure that when there is noincoming light, the signal can be corrected to return a value of zero.In the absorption bands in the infrared region, all light is absorbed,but a non-zero signal can be observed which increases over the durationof the survey. In a representative sample of scenes from each site, thesignal in the absorption bands increases in the scanning direction ofeach scene, indicating the dark current is increasing while the surveyis in process. This may be due to temperature effects from operating indirect sunlight and warm conditions, although each camera has a coolingsystem.

The effect can be mitigated by taking a dark current measurement aftereach scan as well as before. These datasets have been corrected bysubtracting the measurement in the absorption band from every spectralband in the SWIR camera. The dark current in the SWIR camera is muchgreater than the dark current in the VNIR camera, and there are no bandsin the visible to near infrared spectrum where the atmosphere completelyattenuates the incoming light, so the VNIR camera is uncorrected.Comparison of successive scans taken at the same integration time alsoshows that the dark current measured by the VNIR camera does notincrease significantly from one survey to the next, whereas the SWIRcamera does, suggesting that the increase in dark current over thecourse of a survey is less significant or may not occur.

FIG. 4 and FIG. 5 illustrate the mean signal in the atmosphericabsorption bands as a function of the x position of the surveyed scenefor a series of different sites. Each survey was performed withdifferent integration times and different angle bounds, leading todifferent gradients and lengths of each line.

Reflectance calculation: Targets of known spectral reflectance can beplaced in the surveyed scenes in order to convert sensor radiancemeasures to material reflectance. The targets have a uniform (or closeto uniform) and known reflectance across each spectral wavelength. Thereflectance is diffuse, the same from every angle of incidence. Thetargets used with the system have nominal 50% and 99% reflectance, andboth are placed in each surveyed scene. The empirical line method isused to make this correction. This model assumes a linear relationshipbetween the sensor radiance measure and material reflectance, and theradiance of each target is used as a datapoint to compute the gain andoffset for the transform. This is done independently for each spectralwavelength, but the same transform is applied at all locations in thescene. In some scenes, the 99% target is saturated, in which case only again is computed, using the assumption that the line passes through 0.

There are limitations to this method. Firstly, it assumes that bothtargets are under the same illumination, ::which may not be the case ifthey are oriented differently to each other. Secondly, the position ofthe targets only represents a single orientation and illuminationcondition, whereas (a) material in the scene will have many differentorientations and be shadowed differently to the targets (particularlyfor active dig faces where material has been blasted), and (b) theillumination conditions may change over the duration of the scene ifthere is partial cloud. The first can be addressed by comparingillumination invariant measures such as spectral angle, or bynormalising spectral data. In turn, this assumes that the effect ofillumination and orientation variation is constant across eachwavelength. The second can be addressed by adding an irradiance sensorthat is continuously capturing illumination over the duration of thesurvey. Alternative correction methods should also be considered forscenes where it is not feasible or safe to position the reflectivetargets close to the scene being surveyed.

Spectral fusion: After computing the reflectance for each camera, thereis a slight step discontinuity between the reflectance curves for eachcamera. This can be caused by slight errors in the registration of thecameras, slight differences in the incidence angles of light based onthe spatial offset of each camera, and increased noise at theextremities of each camera's spectral range. When a continuousreflectance is needed, one camera's reflectance curve is multiplied by aconstant gain to remove the discontinuity.

Classification

Multiple classification options and how they can be implemented forreal-time classification. These can include SAM, CNN (supervised neuralnetworks), Autoencoder (unsupervised neural networks) and Filteringrelevant bands (band pass).

With further advances in software and hardware, the system can bereduced in size and weight. The prime avenues for this are removing thesecond computer, and reducing the weight of the head unit, allowing fora smaller rotating platform.

Field Trials and Datasets

The system was used to obtain laboratory surveys of ore samples, as wellas field scans at mine sites. The field surveys include dig faces, pitwalls, stockpiles and drill patterns under a variety of illuminationconditions.

Ore samples: Prior to the field trials, each mine sent ore samples todevelop a library of spectral and associated chemical assay data. Afterscanning the ore samples with the system, the samples were sent to alaboratory for analysis. The ore samples were scanned indoors, under abroadband illumination source (halogen lights).

Spectral Quality

There are two significant sources of noise in the hyperspectral data:uncertainty in the photoelectron number, which is the number ofphotoelectrons on the sensor; and uncertainty associated with the darkcurrent, including the artefact correction discussed below.

The uncertainty in the photoelectron number is a result of thestatistical fluctuation in photoelectron number, which is governed by aPoisson distribution. The uncertainty is therefore the square root ofthe signal recorded by the camera with the dark current removed.

Unsupervised Clustering

Unsupervised classification methods address the lack of labelled data inhyperspectral imaging. As hyperspectral images often have manydimensions, and features which distinguish different classes of spectramay occupy few of those dimensions, dimensionality reduction is a keystep in unsupervised methods. The goal is to represent the hyperspectralinformation in a reduced feature space where differences in classes aremore significant, enabling unsupervised methods like clusteringtechniques to better distinguish different data.

The approach here is to use an autoencoder, a symmetric neural networkthat encodes and decodes an input, with the aim of best reconstructingthe input. The loss function used to train the weights is based on thespectral angle of the signatures, with the intention of learning anillumination invariant encoding (Windrim, Ramakrishnan, Melkumyan,Murphy, & Chlingaryan, 2019). This encoding is performed on eachspectral pixel (a one-dimensional method). The encoded spectra are thenclustered using k-means clustering, typically using the same number ofclusters as the number of classes that are expected in the scene.

Unsupervised classification is a good choice for the data from a firstsite scan, as assays were not performed on rock samples taken directlyfrom the scenes scanned, so an independent verification of the classesdoes not exist. The classification problem for iron ore is to be able todistinguish different types of ore— haematite, goethite and magnetite—as well as waste.

It is important to note that each scene was clustered separately, socolours in one scene do not correspond to the same colours in anotherscene. Each scene is classified on a per-pixel basis, which leads tosome noise, but each clustered scene shows regions of dominant classesthat approximately correspond with the expected boundaries. Thelocations of the boundaries between classes differs from the oreboundaries determined from discussion with geologists, which mayindicate real uncertainty in the location of the boundaries, orinaccuracies in the clustering.

Each scene also shows a class that mostly detects shadowed pixels. Whilethere is some invariance to illumination or darkness of the pixels,particularly in the blue class, this suggests the unsupervisedclassification may be picking up on illumination or orientation specificfeatures, rather than material properties. This highlights a limitationof the method used to compute the reflectance of each pixel in thescene. The reflective targets are under constant illumination, whereasthe surveyed face has significant variations in illumination andorientation of the rocks. The autoencoder uses an error measure based onthe spectral angle, which is invariant to a constant change in magnitudeof the spectra across each spectral band. This suggests that the effectof illumination variance may not be independent of the wavelength,leading to a learned feature class which is predominantly shadowedrocks.

The second limitation is that unsupervised classification lackspredictive power. While the unsupervised classification can distinguishbetween different classes, it does not assign labels to those classes.Further work is required to train the autoencoder and to cluster on alarger dataset and test the predictive power on scenes which were notpart of the dataset.

It can be seen that the system: is able to prove processed mine facemaps, in the field, in real-time, to support mine operators andsupervisors implement decisions to maximise mine performance; to befully self-contained and require minimal training to operate, so thatthe system can be used autonomously or semi-autonomously every day atthe mine site, and be sufficiently robust to be able to operate withhigh availability and limited maintenance costs.

The system can also reduce ore dilution, increase ore recovery andoptimise stockpile blending to maximise plant throughout.

Use Cases:-Coal Mine

A number of example use cases for the system can be envisaged. Forexample, in a coal mining system, the uses of the system can include:

TABLE 2 Use cases identified at MAC Ref. Use case UC1 Floor/top of coalclean-up to optimise dilution & recovery UC2 Clay/deleterious materialin coal for washability UC3 Real-time face mapping for weathered zonesand ash content UC4 In-fill bore holes to enhance precision of coalquality model UC5 Real-time stockpile modelling for dynamic tonnagereconciliation UC6 Post wash quality validation UC7 Through seam blastreconciliation and movement to optimise dilution/recovery UC8 Real-timeover-the-conveyor monitoring for short interval control UC9 Longdistance geotechnical mapping for slope stability control UC10 Excavatorfitment for dynamic coal quality tracking

Providing some more details on each use case:

UC1—Floor/top of coal clean-up to optimise dilution & recovery: Purpose:to provide a quantitative system and method that can track and validatethe performance of coal preparation and clean-up to pre-agreedstandards. Potential Benefits: this use case would support themaximisation of coal recovery and minimisation of raw coal dilution.Field testing approach: scan of the area surface, subsequent processedinformation comparison against the RGB image, highlighting aspectsnonvisible to the naked eye.

UC2—Clay/deleterious material in coal for washability: Purpose: toidentify swelling clays from non-swelling clays accurately andquantifiably. Potential Benefits: the application of this use case wouldbe used to support the optimisation of coal handling and processing.Field testing approach: evaluation of the degree of incorporation ofmineral matter in the coal organic matrix based on the recognition andidentification of clay and deleterious material through specificspectral patterns. For the purpose of this study, clays at MAC weredefined into two broad classes, swelling clays and non-swelling clays.It was identified that swelling clays, that is, clays that absorb water,have a deleterious effect on materials handling for the processingplant. As such, the purpose of investigating this UC was to identify arobust technique that could delineate swelling clays from non-swellingclays accurately and quantifiably.

UC3—Real-time dig face mapping: Purpose: to identify variations within amining seam/location, that may not be present in the existing sitegeological model. Potential Benefits: this use case could be applied ona real-time basis for real-time decision support before and duringmining operations. Field testing approach: the data is captured in realtime and integrated with the mine's system. LV system is attached to theback of a vehicle and scans the entire dig face.

UC4—Coal quality model enhancement: Purpose: to create a method thatallows for additional information to be added to the coal quality model,that fits in with the existing ‘bore hole’ method. Potential benefits:this use case could be applied to enhance the coal quality model for themine site, supporting enhanced accuracy of short-term mine scheduling.Field testing approach: Scan coal dig faces, identify spectralvariations and compare against spectral variations in hand samples andassay results to validate potential of the technology for field mapping.

UC5—Real-time stockpile modelling: Purpose: to accurately understand thequality and volume characteristics of coal stockpiles over time.Potential Benefits: the application of this use case could be applied toprovide the processing plant certainty of stockpile characteristicsduring stockpile recovery, for blend and processing optimisation. Fieldtesting approach: scan of multiple positions around the stockpile toidentify and recognize the present material through hyperspectral data,enabling a model to be build-up.

UC6—Post wash rapid coal quality validation: Purpose: to provide asecondary technique, that can be deployed to rapidly and non-invasivelyon product stockpiles. Potential Benefits: this use case could beapplied to confirm that product stockpiles are within their targetspecifications. Field testing approach: scan of multiple positionsaround the stockpile to confirm and distinguish coal from non-coalmaterial.

UC7—Through seam blast reconciliation and coal seam movement. Purpose:To provide a technique, that can accurately determine if coal has beenrecovered as coal or waste. Potential benefits: this use case couldprovide a means for providing guidance for mining, as well asreconciling actual through-seam blast recovery against the plannedrecovery, on a real-time basis. Additionally, this technique could beused to confirm the movement of the coal seam post-blasting. Fieldtesting approach: scan to confirm and distinguish coal from non-coalmaterial boundaries.

UC8—Real-time over-the-conveyor monitoring: Purpose: to provide a methodfor validating raw coal quality characteristics before arriving at theprocessing plant. Potential benefits: This use case could provide a fastand non-invasive method for ‘early warning’ if the input coal isdifferent to the expected coal specifications. Field testing approach:Scan the material that flows over a conveyor belt extracting valuableinformation comparable to a pre-existing database.

UC9—Geotechnical highwall lithology mapping: Purpose: to determine ifmaterials that may be detrimental to slope stability, such as swellingclays and the presence of water can be mapped. Potential benefits: thisuse case could provide new information that enhances geotechnicalmodelling and further reduces the potential for unplanned slope failurewithin active mining areas. Field testing approach: geotechnical dataacquisition without direct contact with the mapping face, from scansintegrated with the system, providing a necessary level of confidence inthe stability of areas.

UC10—Excavator fitment for dynamic coal quality tracking: Purpose: tomonitor coal quality characteristics on a bucket-by-bucket basis.Potential benefits: This UC would provide the necessary information forshort interval control and short-term operational optimisation. Fieldtesting approach: Integrated into the excavator shovel, the system scansvertical lines of the Field of View (FOV) of the entire face while it isdigging allowing decisions to be made on a real-time basis.

Use Cases can be explored during field trials.

The system provides for the provision of real time results for the UseCases. In the field, to support operational decision making and control.The obtained information can further refine and enhance the accuracy ofcoal quality model predictions.

The system also provides: the ability to provide coal maps andassociated data in real-time, mapping of coal from non-coal materials,Identification and mapping of swell from non-swelling clays, andPrediction and modelling of Ash and calorific value.

Some of the benefits of the system will now be discussed:

Real-time mapping and provision of data: A key advantage of the system,is the potential ability to provide data in real-time. In this case,real-time, is defined as being able to obtain the required information,on demand, without any delay that could impact on the timely value ofthe information. From the scans collected, a number of tests wereperformed to determine the reliable functionality of the system inrelation to real-time mapping. On average, a typical 1,500 m² scan couldbe processed within 21 seconds of the data capture phase beingcompleted. The data acquisition phase was found to take between 3-5minutes, depending on the level of input illumination of the scene. As aresult, the system, was able through extensive testing and simulationdemonstrate that real-time mapping could be delivered at MAC. This isparticularly relevant for the mapping of coal from non-coal it isimportant to be able to provide this guidance in the field, inreal-time, because, if a significant time delay occurs between thecapture of data and the communication of information to operators andsupervisors, then the opportunity to influence the dilution and recoveryof coal might be lost. Additionally, the resulting maps could, throughadditional algorithms be re-formatted as virtual boreholes or as inputsdirectly to scheduling software, or any other use case where coal datais required.

Mapping coal from non-coal material: The ability to accurately map coalfrom non-coal materials is important to provide guidance to operationsto maximise coal recovery and minimise dilution. Without this guidance,it can be difficult for mine operators and supervisors to clearlydistinguish between coal and non-coal zones, because many non-coalmaterials, such as shales and mudstones can be often mistaken for coal.To determine the effectiveness of mapping of coal from non-coal, inreal-time, scanning of dig areas was conducted. These scans covered bothtop of coal preparation and coal floor clean-up areas. These areas wereselected on the basis of being the critical interfaces where coal eithercoal recovery is lost, or coal dilution occurs, as an example of thedifficulty of picking coal from other materials visually. This techniquecould be applied, immediately at a coal mine, to provide a trackablemetric for coal clean-up performance. By scanning each clean-up with thesystem, live feedback could be provided to operators and supervisors, aswell as providing a daily metric that could be tracked to ensure thatover time coal dilution is minimised and coal recovery is maximised.This technique could also be used to ‘baseline’ optimal clean-ups byseam, which could be used by the preparation plant, geology and miningdepartments to agree on the standards that should be applied to each theclean-up for each coal type. The system also allows for the ability tomap these coal boundaries with high precision. With adequate coal edgesmapped a correlation of this new information back to the underpinninggeological model can be implemented. This mapping exercise can be usedto determine how much the coal seam moved and if coal losses wereencountered as a result of a through seam blast. A very strongcorrelation existed between captured spectral features outside of thevisible part of the light spectrum and the material type that wasmapped. The technique outlined above could be immediately deployed in areal-time configuration to provide operational support and guidance,resulting in reduced dilution and increased coal recoveries for MAC.

Identification of swelling & non-swelling clays in coal: Theidentification of swelling from non-swelling clays was addressed in twoparts. Firstly, the determination of the existence or not of clay in ascanned scene and then, by determining the type of clay captured. As anexample of the technique, large variations in the abundance of aluminiumclay can be mapped in the coal seam.

In addition to the clay absorption analysis, different aspects ofmineralogy can be mapped by combining spectral measures of ferrous iron,water and clay absorption, and a distribution of these variables in theimage produced. Maps of mineral types derived from spectral matchingwith a spectral library can show different types of clays on the minewall.

To further the reliability of these measures, swelling clays,highlighted previously as Smectites can be identified by the shape oftheir primary absorption feature between 2000-2500 nm. Smectites are thedominant clay group that have swelling properties and can swell up to4-5 times their volume when wet. Smectites (including Al—OH and Fe— OHsmectites) have distinctive absorption features in this spectral region.Both swelling and non-swelling clays (e.g. kaolinite) can be mapped bycomparing (unknown) pixel spectra with known library spectra of clayminerals. FIG. 6 shows typical spectra of coal, swelling clay (smectite)and non-swelling clay (kaolinite).

Two example independent methods can be used to map and cross-correlateswelling clay on the mine faces. The first used the method of (6) toestimate the total amount of water contained within the clay i.e. bothstructural and non-structural (adsorbed) water. This method did notdepend on the particular cation in the clay but instead used an estimateof the amount of water contained in all clays. The second methodcombined information from the classified image of clay type andinformation on the abundance of clay determined from analysis of theprimary Al—OH clay absorption feature, see (7,8) for details.

As a further example of this technique, the classification image can beused to identify swelling clays and then the intensity of the absorptionfeature was used to map their abundance. The abundance of water on themine face can shows distinct layering with layers exhibiting largeamounts of absorption by water being indicative of smectite clay.

The technique described in this section can be important in mappingswelling clays from non-swelling clays in coal seams, as well ashighwalls. As such, the technique has potential to add immediate valuefor both coal materials handling and to provide additional informationfor geotechnical modelling.

Complementing the determination of ash content, a strong correlation wasdrawn with calorific value. As such, the techniques outlined in thissection can be applied with high degree of certainty as a method formapping calorific value of a dig face or coal stockpile.

Overall, the system provides the capability to be able to makepredictions for ash or calorific value. The system allows the ability todistinguish between coal and non-coal materials, resulting in theopportunity to reduce coal dilution and simultaneously increase coalrecovery, Distinguish between swelling and non-swelling clays forenhanced management of coal handling and ability to improve geotechnicalslope stability mapping, and Routinely determine coal stockpilecharacteristics for reserve reconciliation and optimisation of coal feedto the processing plant. The coal information generated, can also beused in parallel to provide additional information, that could be usedto update short-term plans and enhance geological models.

One example specification of the technical components can be as follows:

Sub-component Key specification Purpose LiDAR Operating range 100 mProvide high-resolution spatial Channels 16 information for theconstruction of Angular resolution 2° accurate 3D maps of mine faces.Accuracy ± 3 cm Hyperspectral Spectral range 400-2,500 nm Provide highlyaccurate hyperspectral sensors F/# > 2.0 data across a suitable spectralrange Field of view > 16° that can be input into processing Spectralbinning < 6 nm algorithms, in real-time for Peak SNR > 255classification of mine faces. RTK-GPS Accuracy < 1 cm Provide supportfor 6 degrees of Modes GPS, GLONASS, BeiDou freedom (6DoF) poseestimation for high precision positioning of face maps within anexisting mine survey Power Deep cycle lead acid battery Provide safe andreliable power source for operating in the mine for continuousoperations. Computing Rugged computer system with 1 Tb Provide‘on-board’ ability to process SSD storage and store captured data, forin-field publication of face maps as well as ability to hold sufficientdata, that can be periodically transferred to a cloud

Interpretation

Reference throughout this specification to “one embodiment”, “someembodiments” or “an embodiment” means that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present invention. Thus,appearances of the phrases “in one embodiment”, “in some embodiments” or“in an embodiment” in various places throughout this specification arenot necessarily all referring to the same embodiment, but may.Furthermore, the particular features, structures or characteristics maybe combined in any suitable manner, as would be apparent to one ofordinary skill in the art from this disclosure, in one or moreembodiments.

As used herein, unless otherwise specified the use of the ordinaladjectives “first”, “second”, “third”, etc., to describe a commonobject, merely indicate that different instances of like objects arebeing referred to, and are not intended to imply that the objects sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

In the claims below and the description herein, any one of the termscomprising, comprised of or which comprises is an open term that meansincluding at least the elements/features that follow, but not excludingothers. Thus, the term comprising, when used in the claims, should notbe interpreted as being limitative to the means or elements or stepslisted thereafter. For example, the scope of the expression a devicecomprising A and B should not be limited to devices consisting only ofelements A and B. Any one of the terms including or which includes orthat includes as used herein is also an open term that also meansincluding at least the elements/features that follow the term, but notexcluding others. Thus, including is synonymous with and meanscomprising.

As used herein, the term “exemplary” is used in the sense of providingexamples, as opposed to indicating quality. That is, an “exemplaryembodiment” is an embodiment provided as an example, as opposed tonecessarily being an embodiment of exemplary quality.

It should be appreciated that in the above description of exemplaryembodiments of the invention, various features of the invention aresometimes grouped together in a single embodiment, figure, ordescription thereof for the purpose of streamlining the disclosure andaiding in the understanding of one or more of the various inventiveaspects. This method of disclosure, however, is not to be interpreted asreflecting an intention that the claimed invention requires morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive aspects lie in less than allfeatures of a single foregoing disclosed embodiment. Thus, the claimsfollowing the Detailed Description are hereby expressly incorporatedinto this Detailed Description, with each claim standing on its own as aseparate embodiment of this invention.

Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe invention, and form different embodiments, as would be understood bythose skilled in the art. For example, in the following claims, any ofthe claimed embodiments can be used in any combination.

Furthermore, some of the embodiments are described herein as a method orcombination of elements of a method that can be implemented by aprocessor of a computer system or by other means of carrying out thefunction. Thus, a processor with the necessary instructions for carryingout such a method or element of a method forms a means for carrying outthe method or element of a method. Furthermore, an element describedherein of an apparatus embodiment is an example of a means for carryingout the function performed by the element for the purpose of carryingout the invention.

In the description provided herein, numerous specific details are setforth. However, it is understood that embodiments of the invention maybe practiced without these specific details. In other instances,well-known methods, structures and techniques have not been shown indetail in order not to obscure an understanding of this description.

Similarly, it is to be noticed that the term coupled, when used in theclaims, should not be interpreted as being limited to direct connectionsonly. The terms “coupled” and “connected,” along with their derivatives,may be used. It should be understood that these terms are not intendedas synonyms for each other. Thus, the scope of the expression a device Acoupled to a device B should not be limited to devices or systemswherein an output of device A is directly connected to an input ofdevice B. It means that there exists a path between an output of A andan input of B which may be a path including other devices or means.“Coupled” may mean that two or more elements are either in directphysical or electrical contact, or that two or more elements are not indirect contact with each other but yet still co-operate or interact witheach other.

Thus, while there has been described what are believed to be thepreferred embodiments of the invention, those skilled in the art willrecognize that other and further modifications may be made theretowithout departing from the spirit of the invention, and it is intendedto claim all such changes and modifications as falling within the scopeof the invention. For example, any formulas given above are merelyrepresentative of procedures that may be used. Functionality may beadded or deleted from the block diagrams and operations may beinterchanged among functional blocks. Steps may be added or deleted tomethods described within the scope of the present invention.

1-8. (canceled) 9: A method for detecting changes in the ore grade of arock face, the method including the steps of: (a) providing a scanningsystem having at least a hyperspectral imager, a position system, aLiDAR or range determination unit and computational resources; (b)determining a location of the scanning system utilising the positionsystem; (c) scanning the rock face with the range determination unit todetermine rock face position information; (d) scanning the rock facewith the hyperspectral imager to produce a corresponding rock facehyperspectral image; (e) utilising the computational resources to fusetogether the rock face position information and the corresponding rockface hyperspectral image to produce a rock face position and contentinformation map of the rock face, wherein utilising comprises:determining an initial rock face image using the rock face positioninformation and the determined location; and mapping each portion of aplurality of portions of the rock face hyperspectral image to acorresponding portion in the initial rock face image based on spatialcalibration parameters of the hyperspectral imager to produce the rockface position and content information map of the rock face. 10: A methodas claimed in claim 9 wherein step (c) includes forming a point cloud ofthe rock face position and said step (e) includes determining a contentinformation map for points of the point cloud. 11: A method as claimedin claim 9 wherein the point cloud position is referenced, in theinitial rock face image, relative to the location of the positioningsystem. 12: A method as claimed in claim 9 wherein the hyperspectralimage sensors are calibrated to mitigate the impact of at least one ofdark current, smile, keystone, bad pixels and other sensor specificerrors. 13: A method as claimed in claim 9 further includingsimultaneously sensing the atmospheric lighting conditions andprocessing the captured hyperspectral image to account for lightingconditions. 14: A method as claimed in claim 9 wherein said step (e)further includes utilising machine learning algorithms to classify thematerial in the content information map. 15: A method as claimed inclaim 9 wherein the rock face is a near vertical rock face. 16: A methodas claimed in claim 9, wherein each portion of a plurality of portionsof the rock face hyperspectral image is mapped to a correspondingportion in the initial rock face image by ray tracing based on thespatial calibration parameters of the hyperspectral imager. 17: A methodas claimed in claim 9, wherein scanning the rock face with thehyperspectral imager further comprises: capturing a first hyperspectralimage using a first hyperspectral camera of the hyperspectral imager;capturing a second hyperspectral image using a second hyperspectralcamera of the hyperspectral imager, the first hyperspectral camera andthe second hyperspectral camera being offset from a centre of rotationof the scanning system, wherein a frame rate of each hyperspectralcamera is synchronised with rotation of the scanning system; andspatially fusing image data of the first hyperspectral image and thesecond hyperspectral image onto the initial rock face image based onspatial calibration parameters of each of the first hyperspectral cameraand the second hyperspectral camera. 18: A method as claimed in claim17, wherein the second hyperspectral image and the first hyperspectralimage are captured synchronously. 19: A method as claimed in claim 17,further comprising: spectrally fusing image data of the firsthyperspectral image and the second hyperspectral image. 20: A method asclaimed in claim 19, wherein spectral fusion is based on at least onereflective target of known spectral reflectance captured in the firsthyperspectral image and the second hyperspectral image. 21: A method asclaimed in claim 17, wherein the first hyperspectral image and thesecond hyperspectral image are spatially fused onto the initial rockface image based on the spatial calibration parameters of the firsthyperspectral camera at the time of capture of the first hyperspectralimage and the spatial calibration parameters of the second hyperspectralcamera at the time of capture of the second hyperspectral image. 22: Amethod for detecting changes in the ore grade of a rock face, the methodincluding: determining an initial rock face image using rock faceposition information obtained by scanning the rock face with a rangedetermination unit, each portion of the initial rock face image beingassociated with a geo-referenced coordinate and rock face range data;determining a corresponding rock face hyperspectral image using dataobtained by scanning the rock face with a hyperspectral imager havingspatial calibration parameters; and fusing together the initial rockface image and the corresponding rock face hyperspectral image bymapping each portion of a plurality of portions of the rock facehyperspectral image to a corresponding portion in the initial rock faceimage based on the spatial calibration parameters of the hyperspectralimager to produce a rock face position and content information map ofthe rock face. 23: A method as claimed in claim 22, further comprising:capturing a first hyperspectral image using a first hyperspectral cameraof the hyperspectral imager; capturing a second hyperspectral imageusing a second hyperspectral camera spaced apart from the firsthyperspectral camera; and spatially fusing image data of the firsthyperspectral image and the second hyperspectral image onto the initialrock face image based on spatial calibration parameters of each of thefirst hyperspectral camera and the second hyperspectral camera. 24: Amethod as claimed in claim 23, wherein the second hyperspectral imageand the first hyperspectral image are captured synchronously. 25: Amethod as claimed in claim 23, further comprising: spectrally fusingimage data of the first hyperspectral image and the second hyperspectralimage. 26: A method as claimed in claim 24, wherein spectral fusion isbased on at least one reflective target of reference spectralreflectance captured in the first hyperspectral image and the secondhyperspectral image. 27: A method as claimed in claim 23, wherein thefirst hyperspectral image and the second hyperspectral image arespatially fused onto the initial rock face image based on the spatialcalibration parameters of the first hyperspectral camera at the time ofcapture of the first hyperspectral image and the spatial calibrationparameters of the second hyperspectral camera at the time of capture ofthe second hyperspectral image. 28: A system for detecting changes inthe ore grade of a rock face, the system comprising: a scanning systemcomprising a hyperspectral imager, a position system and a LiDAR orrange determination unit, wherein the position system is configured toprovide a location of the scanning system; the range determination unitis configured to scan the rock face to determine rock face positioninformation, and the hyperspectral imager is configured to scan the rockface to produce corresponding rock face hyperspectral image data; and acomputational resource coupled with the scanning system and configuredto execute instructions to: receive the location of the scanning system,the rock face position information and the corresponding rock facehyperspectral image data; and determine initial rock face image datausing the rock face position information and the determined location;and fuse together the initial rock face image data and the correspondingrock face hyperspectral image data by mapping each portion of aplurality of portions of the rock face hyperspectral image data to acorresponding portion in the initial rock face image data based onspatial calibration parameters of the hyperspectral imager to producethe rock face position and content information map of the rock face.